Model comparison
Gemini 3.5 Flash vs GPT-5.6 Terra
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 54.1 on the Noometry Index.
Last verified . 46 shared benchmarks.
GoogleSummary
- They share 46 benchmarks with published results for both. Gemini 3.5 Flash scores higher in 5 categories and GPT-5.6 Terra in 5 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 60.5.
- The biggest single-benchmark swing is FrontierMath Tier 4: 26.8% for Gemini 3.5 Flash and 70.7% for GPT-5.6 Terra.
- Gemini 3.5 Flash is cheaper at $1.50 / $9 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 1.05M.
Side by side
| Gemini 3.5 Flash | GPT-5.6 Terra | |
|---|---|---|
| Provider | OpenAI | |
| Noometry Index | 54.1 | 59.1 |
| Released | 2026-05-19 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 66K | 128K |
| Input $ / M tokens | $1.50 | $2 |
| Output $ / M tokens | $9 | $12 |
| Results tracked | 54 | 52 |
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Category by category
Coding GPT-5.6 Terra leads
Gemini 3.5 Flash: 49.1 (#46), GPT-5.6 Terra: 57.1 (#16)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| DeepSWE | 37.4% | 69.6% |
| LMArena WebDev | 1499 | 1520 |
| SciCode | 53.1% | 55% |
| WeirdML | 62.6% | 78.3% |
| LMArena Coding | 1492 | 1484 |
| ALE-Bench | 911.02 | 1,951 |
| SWE-bench Verified | 79.3% | — |
| FrontierCode | — | 41.3% |
| CursorBench | — | 41.3% |
Agentic & Tool Use GPT-5.6 Terra leads
Gemini 3.5 Flash: 24.8 (#107), GPT-5.6 Terra: 40.2 (#23)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| APEX-Agents | 27.5% | 58.2% |
| GDP.pdf | 14% | 24.7% |
| Vending-Bench 2 | 5,396 | 7,343 |
| BALROG | — | 53.2% |
| GBAEval | 6.7% | — |
Reasoning Gemini 3.5 Flash leads
Gemini 3.5 Flash: 62.7 (#16), GPT-5.6 Terra: 60.6 (#19)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| ARC-AGI-2 | 72.1% | 83.9% |
| SimpleBench | 76.7% | 48.9% |
| NYT Connections (extended) | 92.6% | 78.4% |
| ARC-AGI-1 | 92.5% | 96.5% |
| CritPt | 13.1% | 30% |
| Chess Puzzles | 50% | 54% |
| LMArena Hard Prompts | 1488 | 1467 |
| Mystery Game Puzzles | 32% | 35% |
| DTBench | 94.7% | 93.3% |
| LMCA | 47.1% | 55% |
| Surface Evolver Bench | 58.1% | 83.8% |
| Epoch Capabilities Index | 154.55 | 159.31 |
| Kagi LLM Benchmark | — | 51.3% |
| EnigmaEval | 25.4% | — |
| EBR-Bench | 4.8% | — |
| ForecastBench | 59 | — |
Math GPT-5.6 Terra leads
Gemini 3.5 Flash: 60.5 (#30), GPT-5.6 Terra: 81.5 (#10)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| FrontierMath (Tiers 1-3) | 62.8% | 86% |
| FrontierMath Tier 4 | 26.8% | 70.7% |
| OTIS Mock AIME 2024-2025 | 95.6% | 99.7% |
| ProofBench | 31% | 74% |
| LMArena Math | 1502 | 1463 |
| MathArena Final-Answer Competitions | 76.3% | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge Gemini 3.5 Flash leads
Gemini 3.5 Flash: 66.3 (#10), GPT-5.6 Terra: 61.3 (#27)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| GPQA Diamond | 92.8% | 93.3% |
| SimpleQA Verified | 66.2% | 43.2% |
| LMArena Expert | 1493 | 1492 |
Multimodal GPT-5.6 Terra leads
Gemini 3.5 Flash: 45.7 (#13), GPT-5.6 Terra: 47.5 (#10)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Vision | 1311 | 1271 |
| Blueprint-Bench 2 | 33.6% | 30.8% |
| LMArena Document | 1463 | 1472 |
| Furniture Assembly | — | 54.2% |
Multilingual Gemini 3.5 Flash leads
Gemini 3.5 Flash: 57.1 (#12), GPT-5.6 Terra: 54.5 (#42)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Non-English | 1475 | 1439 |
| LMArena Chinese | 1525 | 1514 |
| LMArena French | 1490 | 1476 |
| LMArena German | 1500 | 1462 |
| LMArena Japanese | 1481 | 1446 |
| LMArena Korean | 1453 | 1420 |
| LMArena Russian | 1491 | 1450 |
| LMArena Spanish | 1478 | 1455 |
Instruction Following Too close to call
Gemini 3.5 Flash: 77.2 (#29), GPT-5.6 Terra: 76.6 (#38)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Instruction Following | 1466 | 1453 |
Long Context Too close to call
Gemini 3.5 Flash: 45.3 (#39), GPT-5.6 Terra: 44.4 (#64)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Longer Query | 1481 | 1452 |
Writing & Preference GPT-5.6 Terra leads
Gemini 3.5 Flash: 65.5 (#46), GPT-5.6 Terra: 70.4 (#21)
| Benchmark | Gemini 3.5 Flash | GPT-5.6 Terra |
|---|---|---|
| LMArena Text | 1481 | 1446 |
| LMArena Creative Writing | 1467 | 1411 |
| EQ-Bench 4 | 1087 | 1234 |
| LMArena Multi-Turn | 1482 | 1451 |
| EQ-Bench Creative Writing | — | 1855 |
Frequently asked questions
Is Gemini 3.5 Flash better than GPT-5.6 Terra?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 54.1 on the Noometry Index.
Which is cheaper, Gemini 3.5 Flash or GPT-5.6 Terra?
Gemini 3.5 Flash is cheaper. It lists at $1.50 per million input tokens and $9 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is Gemini 3.5 Flash or GPT-5.6 Terra better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 49.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 1.05M.
How many benchmarks do Gemini 3.5 Flash and GPT-5.6 Terra share?
46 benchmarks have published results for both models. Gemini 3.5 Flash has 54 scored results on Noometry and GPT-5.6 Terra has 52.